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Blood–Brain Barrier Function and Biomarkers of Central Nervous System Injury in Rickettsial Versus Other Neurological Infections in Laos
Blood–brain barrier (BBB) function and cerebrospinal fluid (CSF) biomarkers were measured in patients admitted to hospital with severe neurological infections in the Lao People's Democratic Republic (N = 66), including bacterial meningitis (BM; N = 9) or tuberculosis meningitis (TBM; N = 11), Japanese encephalitis virus (JEV; N = 25), and rickettsial infections (N = 21) including murine and scrub typhus patients. The albumin index (AI) and glial fibrillary acidic protein (GFAP) levels were significantly higher in BM and TBM than other diseases but were also raised in individual rickettsial patients. Total tau protein was significantly raised in the CSF of JEV patients. No differences were found between clinical or neurological symptoms, AI, or biomarker levels that allowed distinction between severe neurological involvement by Orientia tsutsugamushi compared with Rickettsia species.
A Novel Technique for Detecting Antibiotic-Resistant Typhoid from Rapid Diagnostic Tests
ABSTRACT Fluoroquinolone-resistant typhoid is increasing. An antigen-detecting rapid diagnotic test (RDT) can rapidly diagnose typhoid from blood cultures. A simple, inexpensive molecular technique performed with DNA from positive RDTs accurately identified gyrA mutations consistent with phenotypic susceptibility testing results. Field diagnosis combined with centralized molecular resistance testing could improve typhoid management and surveillance in low-resource settings.
Causes of Fever in Rural Southern Laos
The etiology of fever in rural Lao People's Democratic Republic (Laos) has remained obscure until recently owing to the lack of laboratory facilities. We conducted a study to determine the causes of fever among 229 patients without malaria in Savannakhet Province, southern Laos; 52% had evidence of at least one diagnosis (45% with single and 7% with apparent multiple infections). Among patients with only one diagnosis, dengue (30.1%) was the most common, followed by leptospirosis (7.0%), Japanese encephalitis virus infection (3.5%), scrub typhus (2.6%), spotted fever group infection (0.9%), unspecified flavivirus infection (0.9%), and murine typhus (0.4%). We discuss the empirical treatment of fever in relation to these findings.
Prioritising pathogens for the management of severe febrile patients to improve clinical care in low- and middle-income countries.
BackgroundSevere febrile illness without a known source (SFWS) is a challenge for clinicians when deciding how to manage a patient, particularly given the wide spectrum of potential aetiologies that contribute to fever. These infections are difficult to distinguish clinically, and accurate diagnosis requires a plethora of diagnostics including blood cultures, imaging techniques, molecular or serological tests, and more. When laboratory services are available, a limited test menu hinders clinical decision-making and antimicrobial stewardship, leading to empiric treatment and suboptimal patient outcomes. To specifically address SFWS, this work aimed to identify priority pathogens for a globally applicable panel for fever causing pathogens.MethodA pragmatic two-pronged approach combining currently available scientific data in an analytical hierarchy process and systematically gathered expert input, was designed to address the lack of comprehensive global aetiology data. The expert re-ranked list was then further adapted for a specific use case to focus on community acquired infections in whole blood specimens. The resulting list was further analysed to address different geographical regions (Asia, Africa, and Latin America), and Cohen kappa scores of agreement were calculated.ResultsThe expert ranked prioritized pathogen list generated as part of this two-pronged approach included typhoidal Salmonella, Plasmodium species and Mycobacterium tuberculosis as the top 3 pathogens. This pathogen list was then further adapted for the SFWS use case to develop a final pathogen list to inform product development. Subsequent analysis comparing the relevance of the SFWS pathogen list to multiple populations and geographical regions showed that the SFWS prioritized list had considerable utility across Africa and Asia, but less so for Latin America. In addition, the list showed high levels of agreement across different patient sub-populations, but lower relevance for neonates and symptomatic HIV patients.ConclusionThis work highlighted once again the challenges of prioritising in global health, but it also shows that taking a two-pronged approach, combining available prevalence data with expert input, can result in a broadly applicable priority list. This comprehensive utility is particularly important in the context of product development, where a sufficient market size is essential to achieve a sustainable commercialized diagnostic product to address SFWS.
Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile.
Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.
Target Product Profile for a mobile app to read rapid diagnostic tests to strengthen infectious disease surveillance.
The essential role of rapid diagnostic tests (RDTs) in disease control is compromised every time a test is not performed correctly or its result is not reported accurately and promptly. A mobile app that utilizes the camera and connectivity of a common smartphone can fill this role of supporting the test's proper execution and the automatic transmission of results. In a consensus process with 51 expert participants representing the needs of clinical users, healthcare programs, health information systems, surveillance systems, and global public health stakeholders, we developed a Target Product Profile describing the minimal and optimal characteristics of such an app. We collected feedback over two rounds and refined the characteristics to arrive at a preferred agreement level of greater than 75%, with an average of 92% agreement (range: 79-100%). As per this feedback, such an app should be compatible with many RDTs and mobile devices without needing accessories. The app should assist the user with RDT-specific instructions, include checks to facilitate quality control of the testing process and suggest results with ≥ 95% accuracy across common lighting conditions while allowing the user to determine the final result. Data from the app must be under the control of the health program that operates it, and the app should support at least one of the common data exchange formats HL7, FHIR, ASTM or JSON. The Target Product Profile also lays out the minimum data security and privacy requirements for the app.